{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8d97365b-fe57-446b-8316-b42bec6828f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import os.path\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from scipy.special import erf\n",
    "\n",
    "import refnx\n",
    "from refnx.dataset import ReflectDataset, Data1D\n",
    "from refnx.reflect import SLD, MaterialSLD, ReflectModel, Component\n",
    "from refnx.analysis import Objective, CurveFitter, Transform, possibly_create_parameter\n",
    "from refnx.analysis import Parameter, Parameters\n",
    "from refnx.reflect import SLD, Slab, Structure, ReflectModel\n",
    "\n",
    "np.random.seed(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "aa97c0b0-1b45-4ce5-9ac2-8de8dee598e5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<Pilatus_Interpolated>, 256 points\n"
     ]
    }
   ],
   "source": [
    "#data = ReflectDataset(\"interS/curve5518.txt\")\n",
    "data = ReflectDataset(\"Pilatus_Interpolated.txt\")\n",
    "print(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "11c267a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "class PTCDI(Component):\n",
    "    def __init__(\n",
    "        self,\n",
    "        nlayers,\n",
    "        monolayer_thickness,\n",
    "        rel_roughness,\n",
    "        sld_box1,   # <----- this is closest to the phase layer\n",
    "        sld_box2,\n",
    "        thick_frac,\n",
    "        sig1_rel_pos,  # <------ counting from backing to fronting direction\n",
    "        sig1_width,\n",
    "        sig2_rel_pos,  # <------ counting from backing to fronting direction\n",
    "        sig2_width,\n",
    "    ):\n",
    "        super().__init__()\n",
    "        self.nlayers = possibly_create_parameter(nlayers)\n",
    "        self.monolayer_thickness = possibly_create_parameter(monolayer_thickness)\n",
    "        self.rel_roughness = possibly_create_parameter(rel_roughness)\n",
    "        self.sld_box1 = possibly_create_parameter(sld_box1)\n",
    "        self.sld_box2 = possibly_create_parameter(sld_box2)\n",
    "        self.thick_frac = possibly_create_parameter(thick_frac)\n",
    "        self.sig1_rel_pos = possibly_create_parameter(sig1_rel_pos)\n",
    "        self.sig1_width = possibly_create_parameter(sig1_width)\n",
    "        self.sig2_rel_pos = possibly_create_parameter(sig2_rel_pos)\n",
    "        self.sig2_width = possibly_create_parameter(sig2_width)\n",
    "\n",
    "    @property\n",
    "    def parameters(self):\n",
    "        return Parameters([\n",
    "            self.nlayers,\n",
    "            self.monolayer_thickness,\n",
    "            self.rel_roughness,\n",
    "            self.sld_box1,\n",
    "            self.sld_box2,\n",
    "            self.thick_frac,\n",
    "            self.sig1_rel_pos,\n",
    "            self.sig1_width,\n",
    "            self.sig2_rel_pos,\n",
    "            self.sig2_width,\n",
    "        ])\n",
    "            \n",
    "    def slabs(self, structure=None):\n",
    "        nlayers = int(np.round(self.nlayers.value))\n",
    "        monolayer_thickness = self.monolayer_thickness.value\n",
    "        rel_roughness = self.rel_roughness.value        \n",
    "        s_width1 = self.sig1_width.value\n",
    "        s_width2 = self.sig2_width.value\n",
    "        s_start1 = self.sig1_rel_pos.value\n",
    "        s_start2 = self.sig2_rel_pos.value\n",
    "        sld1 = self.sld_box1.value\n",
    "        sld2 = self.sld_box2.value\n",
    "        diff = sld2 - sld1\n",
    "        mini, maxi = min(sld1, sld2), max(sld1, sld2)\n",
    "\n",
    "        slabs = np.zeros((nlayers * 2, 5))\n",
    "        t1 = monolayer_thickness * self.thick_frac.value\n",
    "        t2 = monolayer_thickness * (1 - self.thick_frac.value)\n",
    "        slabs[:, 0] = np.array([t1, t2] * nlayers)\n",
    "\n",
    "        # why logistic, why not erf?\n",
    "        def logistic(x, mini, maxi, k, x0):\n",
    "            return mini + (maxi - mini) / (1 + np.exp(-k * (x - x0)))\n",
    "            # return 0.5 * (erf(-k * (x - x0))) + 0.5\n",
    "\n",
    "        idx = np.arange(nlayers * 2) / 2.0\n",
    "        \n",
    "        # note that the first monolayer has idx=0, and the last monolayer has idx=29.\n",
    "        # if your indexing starts from 1, then sig1_rel_pos will be different by 1.\n",
    "        sigmoid1 = logistic(idx, 1, 0, s_width1, s_start1)\n",
    "        sigmoid2 = logistic(idx, 1, 0, s_width2, s_start2)\n",
    "        #for i in range(45):\n",
    "        #    if idx[i] > s_start2+2:\n",
    "        #        sigmoid2[i] = 0      \n",
    "\n",
    "        avg_sld = (sld1 + sld2) / 2\n",
    "        diff = maxi - mini\n",
    "\n",
    "        idx = np.arange(nlayers)\n",
    "\n",
    "        if sld1 > sld2:\n",
    "            slabs[2 * idx, 1] = sigmoid1 * ((maxi - avg_sld)*sigmoid2 + avg_sld)\n",
    "            slabs[2*idx + 1, 1] = sigmoid1 * ((avg_sld - mini)*(1 - sigmoid2) + mini)\n",
    "        else:\n",
    "            slabs[2*idx + 1, 1] = sigmoid1[2*idx + 1] * ((maxi - avg_sld)*sigmoid2[2*idx + 1] + avg_sld)\n",
    "            slabs[2 * idx, 1] = sigmoid1[2*idx] * ((avg_sld - mini)*(1 - sigmoid2[2*idx]) + mini)\n",
    "\n",
    "        slabs[:, 3] = rel_roughness * monolayer_thickness\n",
    "        # fronting medium comes first\n",
    "        return slabs[::-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a532eb12",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "# a demonstration cell.\n",
    "#         nlayers,\n",
    "#         monolayer_thickness,\n",
    "#         rel_roughness\n",
    "#         sld_box1,\n",
    "#         sld_diff,\n",
    "#         thick_frac,\n",
    "#         sig1_rel_pos,\n",
    "#         sig1_width,\n",
    "#         sig2_rel_pos,\n",
    "#         sig2_width,\n",
    "c = PTCDI(45, 10, 0.1, 5, 10, 0.5, 10, 1, 10, 0.1)\n",
    "s = SLD(0) | c | SLD(8)\n",
    "# print(c.slabs())\n",
    "s.plot()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "22c66d1d-3e64-4358-8afa-13d1301057c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Yvy8FH4PT0ArOCSeYS3vBIUEGGEsxuNHfxRdfrHXr1unuYHdPBAAgXpWUSKNGSU8+GfweNBs21P8+cGp4uIAj1e2mCgw41oBgy+HD0t69vveVlXUrSdY58vN93wlsk8vH30gxelRDSkqK7rjjjlgcGgCA2LA/t6mkpO72wMASWD2xz26SwndRSXUHGluPUbAEBp5g99cJ3Cewi8oekqjgAACQYD791Le+Y0fd7eGqKw2p4FifWQN8Q1VwOnQIfg4rAOXkSMnJ5npgkAqs4BBwAABIYPZAEizgBIaNwMBjhZUTT6x7PIsVPr73Pf/vBB6zY8fg5zxyxFy2aOHrZgqsJAULONZgZgIOAAAJxh5IAp/fJPnChxUsAgOOVV0pKKh7PMkcK2N91q2bubTf88Z+zIYEHOtRC+EqODU1vkHTzKKq36effqqtW7d637/00ksaMmSICgsLNXToUM2cOTNqDQQAoEnYx8fU10VlhY/AgGOFj9xcc1lR4T8NvLLS9z4vz/87FiskhQo41oDhjIyGV3Ds36OCU7/rrrtOX375pSTp2Wef1S9+8Qv1799f999/vwYMGKAbbrhBz4e6jTUAAPHGMBreRZWTYy7DBZyaGv+AYgWP9HTzYZtS3YdlWse0znG8FRzD8J2nY0cpKcn/OAk0iyolki9t3rxZPXr0kCQ9+eSTeuKJJ3TDDTd4tw8YMECPPPKIxo0bF51WAgAQS4cOmYHEUlZmftaype+zcBUcK6y0b28OAK6pMQNFq1a+Y0pmuLEe6RBYwQnXRWWv4KSlmev2gFNV5fsdbdqY7T9woG7AoYITXMuWLbXnvzcf2rFjhwYOHOi3fdCgQX5dWAAAxDXrD39Ski/UBFZxGlpdadnSVyGxV4Ws9exsM6BIoSs49oBj7+ayzpGR4avg2Luo7IEpI8P3Wwg4DVNUVKSnnnpKknTOOefoH//4h9/21157Td27d2986wAAaAr2P/xt25rrgYN3rZAQroJjHx9jP4ZVwcnKangFxz5A2H4O+yyqwAqOJTU1oQNORF1Uv//97zVkyBCdc8456t+/v/74xz9q4cKF6tWrlzZt2qRly5bpzTffjHZbAQCI3KxZ0sKF0m9+4wsxFvsf/pT//mm0V1dqa8MHHPv4GCtA2Cs41vdbtQpdwQkcZGx9z+qOamgFJyND8nh8Acc6D7Oo6pefn69Vq1Zp8ODBmjt3rgzD0IoVK/Tvf/9bBQUF+vDDD3XhhRdGu60AAETuV7+SnnhC6tlTOnbMf5s94FjVFXsXlD2IhBpkbK/gBAs4VnUlPd0XcEJVcNq29d3Iz96OYPfBCVbBSU/37Wc/BhWc8LKzszV58mRNnjw5mu0BACD6DMP3cMzdu82ndPfp49tu7z6yQoQ91NhDhnWX4foqOIGVE/v2jIzwXVStW5vHqKwMHrQaUsGR/Luoamt9+ybALKqo3ejvww8/VJW97w8AgHgR+Awo+wMrpeAVHHs4sYKHPViECjgZGb4Kiv3vYrAKjv0chuE7ZqtWdcfPBJ6jvhBlnd9+DPt9eBKgghO1gFNUVKQdwe4bAACA0wIfnhkq4NincAcLOK1a+aZ919dFFawLKlwF5+hR3xTvUAHHPsi4vhAVrIJj/ca0NN92F4tawDHs09gAAIgngQ/C/O+tTrzCVXDsA4StgFPfTfgiqeDYnyTekAqOdY5gISpwDM7hwwk1/kbiWVQAgEQQGHAi7aJq2dK/gmP/P/eNreBY50hLM2dyhavgWOdoaAUngWZQSVEMOM8884xyrdtTAwAQT8J1UVmDb627/0qhu6is7bW1/uGisRUc+zmkhldwgrUh2BicBKvgRDyLKtBPf/rTaB0KAIDoCtdFZQ8nDe2iksxQEhhWIq3gWOewQkm4gBPsHA0Zg5MAM6ikKFRwPvjgA11zzTU666yzvIOMX3rpJS1ZsqTRjQMAICqsgNOzp7kMrOAECx+huqhSUnw33rMPNG5sBae+cGIJN8iYCo5XowLOP//5T40cOVItWrTQp59+6p0mXl5ert/97ndRaSAAAI1mdVGdcoq5DAw49vARbhaVfWl9bn+kQkMqONb2mhrfTQcDb9IXySDjwJDEIOPITJw4UU8//bT+9re/KTU11fv5kCFD9Omnnza6cQAARIU1Q6lrV3MZqosqVAXH3kVlX1oBxx4y7NWVYOHDHqLs+4S6SZ+9ShRukHF9xyDgNNymTZs0bNiwOp9nZWWpLPCmSgAAOMUKBgUF5vJ4u6isgGNtCxxDYw8y9i6oUOHDfh8a6zyhHrMQqgoULkRJ5h2RJTPgEXAaLi8vT1u2bKnz+ZIlS9TVSskAADjNChGFheZy/37fTfWk8AEnsOsncPyLtW9Kivmqr4sqPV1KSqr7AM3A8TP1jbFpaAXH/jgHpok33A033KDbbrtNy5cvl8fj0c6dO/XKK6/ozjvv1M033xytNgIA0DhWBaZTJ3NpGP431gs3BicwOAQGGHvwsI5jP659PfAYgRWcUOew79uQx0FIvgpOZWXCzaJq1DTxe++9V7W1tRo+fLgOHTqkYcOGKT09XXfeeaduvfXWaLURAIDGsYJBdrbk8ZgB5/BhXzUjXAUnXMCxB49g2+3r9i6oigrf54HhpKHdXNZA5ZSU0BWcAwd83XLt2ysRNCrgeDwe3X///brrrru0ZcsWHThwQL1791ZrKzECABAP7INzW7QwKzqhZifV10UVKnxEo4ITGE6CjbEJNk3cOnZKSt122ruo9u0z19u1UyKIyo3+0tLS1Lt372gcCgCA6Kqt9YWEli19ASdUgGlIBScwfISr8ATbJ3AQcagQZW03DP+QZA84R46YM7vqG4OT9N9RKQlSwYnJs6i2b9+ucePGxeLQAAAcn8Ap3OECzPFUcAK7qBpSwQk8RrgKTmCVyDpPSoqUnOy/T6gKztGj0u7d5nqCVHBiEnD27dunF154IRaHBgDg+NiDSrCAYxiNH4MTSQUncJ9wIcp+rHAhKLCCY5cgASeiLqrZs2fXu/2rr76KqDEAAERd4BTuwABz7JjZjSWFDzihpnBHUsEJdS+dcAEnKcn8HdY+hw6FDknWlPVgz9pyuYgCzuWXXy6PxyPD/pj4AB6PJ+JGAQAQNYEPsQwMMPYQEmoMTrgp3I2p4ISaJl5fiLL+xoar4EhmFcf6PEHG30gRdlF16tRJb7zxhmpra4O+eEwDACBuBFZXrKATOPZF8g841dW+mwEe7zTxwOBhHyAcqoLT0C4qe3gJHOwceAzJv5sqQbqnpAgDTr9+/bRy5cqQ28NVdwAAaDKBAcdaWpUdKxykppqDdu1dOKEeoxCqcmJ9NzCcBHaD2ZcNHWQc+Dvs+4ar4FgSqIITURfVXXfdpYP2h38F6N69u95///2IGwUAwHExDGnkSGnnTmnZMt8dfCVfMAjXRRVYWbH2ad36+AcZW8eybsIX2A1mP080KjihZlFJ/teCCk79zj77bF1wwQUht7dq1UrnnHNORA1avHixLrnkEuXn58vj8WjWrFl+2w3D0AMPPKBOnTqpRYsWGjFihDZv3hzRuQAALrFzpzR/vrR+vTRjhv+2UA/KDFU5sT8nKlQICjdN3B5CqqrqdoPZ9wlsR7hz2I/dkBCUoBWcmEwTb4yDBw/qtNNO09SpU4Nuf/TRRzVlyhQ9/fTTWr58uVq1aqWRI0fqiP0fDwAgsdjHfr70kv+2UF1UoQJOQ/YJ1zUUeBM+az/7vWtCTRMPNwXcXmFqSAUnQcfgROVOxtFUVFSkoqKioNsMw9Djjz+u//f//p8uu+wySdKLL76o3NxczZo1S1dddVXQ71VVVanKVh6ssJ6oCgBwB3vA+eAD88Z2gVWY4w045eUNf4xC4DmsIFNT41/BCRaiGtNFFWqfUAGnVy8lirir4NRn69atKikp0YgRI7yfZWVladCgQVq6dGnI702aNElZWVneV2FhYVM0FwDQVAJn71oPlpTqjsEJnEUVrOoRbpzO8YaPYOdo6CDjo0d9Dwe1t82+T+BAZOs3StK33/rWL71UiaJZBZySkhJJUm5urt/nubm53m3BTJgwQeXl5d7X9u3bY9pOAEAT++wz//f2gBNqDE7gLKpQXVSBz4CyL0NVcCT/8NGYCk59x7BXkgyj7j1/JKljR996drYSRUQB54EHHqh3mni8SU9PV2Zmpt8LAOAi+/f7vw9WwYl0DM7Ro77Pw00TD1XBCdZ11NBBxta2+gYZV1WZ9+2xpqLbg9bkydL110ubNimRRBRwvv32WxUVFamgoEA333yz3n33XR21/wOIkby8PElSaWmp3+elpaXebQCABGMY5tOyJenEE81lfV1U4bqf7PsePhz8GVDHO4Xbqqy0auXbHqqCYx0jNdW3r72CE6pKZJ3D3n5JKiiQnn1W+t73lEgiCjjPP/+8SkpKNGPGDLVp00a33367OnTooCuvvFIvvvii9u3bF+12SpK6dOmivLw8LViwwPtZRUWFli9frsGDB8fknACAOFdV5bvjcLCAc7zTxAP3sd/Dxhq43JAuKnuFJljXUahHNVihxePxP0+4aeLWOZKT/cNRgop4DE5SUpLOPvtsPfroo9q0aZOWL1+uQYMG6ZlnnlF+fr6GDRumP/zhD9qxY8dxHffAgQNavXq1Vq9eLckcWLx69Wpt27ZNHo9Ht99+uyZOnKjZs2frs88+089+9jPl5+fr8ssvj/SnAACaM6t6I9VfwYk04Ni7jo7nGVBWmDl0SLJujltfBaeh3VzBKjj2AGR/VlUCi9o08V69eqlXr166++67tXv3bs2ePdv71PE777yzwcf55JNP9IMf/MD7fvz48ZKkMWPGaPr06br77rt18OBB3XjjjSorK9PQoUM1d+5cZdj/QQAAEocVcFq29A2orS/ghHoWVbiAU9/07GAVHCvMHDwYWQXHvh5uoHKoKlECi8l9cDp27Kjrr79e119//XF/99xzzw37lPLf/va3+u1vf9uYJgIA3MIKOG3a+O7U29gxOMG6qIINEK6v+tLQgFPfPWzCdVFZj2Gwn8MeshJYs5omDgBAHQcOmMvWrYMHnMZOEz+eG+yFquCE66KqrTWfWRV4nmAVnGDnOHAg+D1wEhgBBwDQvDW0ghNqDE6wyki4Ck6oG+xFUsEJHMjc2AoOAUcSAQcA0Nw1tovKqq7Yn7rd0AqO9bTwcBWcYOHDXsEJNhXdvh5qkLG9gkMXlZ+4exYVAADHxeqiamgFxz67Saq/+yhwFpWlITfhCxZw7OewV3Cs7yclmc+xsoQbZGyv4NBF5SfigFNbW6vp06frjTfe0Ndffy2Px6MuXbroRz/6ka699lp5mKIGAGgKVgWndWvfgyWt0CPVrWxY+xw6ZFZgrH1DBZzAG/BJdZ8WHq6CEyx8WPsePWo+2FOSMjP9p3gH66IKV8Eh4EiKsIvKMAxdeuml+vnPf64dO3aob9++OuWUU/TNN99o7NixGjVqVLTbCQBAcPYuKiu8HD7sG7QbGAzsXVH2AcDH00WVnOyrtFjhJHCfho7BkaRdu8xl4KOE7Pe5CVfBoYvKT0QVnOnTp2vx4sVasGCB3z1rJOm9997T5ZdfrhdffFE/+9nPotJIAABCsgecwPCSlVW3epKRYQYUq3oTSReVZJ6rrEzavdv3WbCAc+iQrw3BuqgkyXoEUWDAsbejvm4wZlHVEVEFZ8aMGbrvvvvqhBtJOu+883TvvffqlVdeaXTjAAAIyz5NPD3dV1mxgk9gBcfj8QWhysrwXVT2AGVnPZm7pMRcJiX5PyIhXAUnNdUMWlLogJOVZS7Ly4N3gzGLKqSIAs7atWt1wQUXhNxeVFSkNWvWRNwoAAAazB5A7OHFCi7Bum7sY3XCdVGVlZnrVqCxWOHDCjgZGf7jZ8IFHOs7kq+LyjqmpW1bc7l/f/0VnOpqXzvpopIUYcDZt2+fcnNzQ27Pzc3V/sBH1wMAEAuBFRZrWVlpjsOxxuLYw4W9ghOui8oaYxMYPgIrOIHBwjpfqFlU9u+EGoNjnaOsrP6BzJKvq4wKjqQIA05NTY1SUkIP30lOTtYx6x8UAACxZJ8mbl9WVvqqHlLoCk64LqrjqeDYBbuTcagKTqguKuuc+/f7QpL9PGlpvm4xAo6fiAYZG4ahsWPHKj1wwNV/VdnvyAgAQCzZp4nbl/aBt1Lw2Uf2Ck6oLqpIKzj2gFNdba4Hho9wFRyri2rnTt9vse71Y/8t+/f7Ag5dVJIiDDhjxowJuw8zqAAATSKw+8dewbFXPezjY6x99u3zhY9IKzg7d/p/x2IPOFavRmAXVUMrOJs3m8u0tLr7tGplBhwrJFHBkRRhwJk2bVq02wEAQGQCB/AGq+AE/tG39rGChRQ84Bw6FL6Cs2mTuQwcmxos4AS2wzrG11+by1AVnH37zGXHjv5Bzf5b6KLyE5NnUX377be68cYbY3FoAAD8BQacYGNwAqsr1j5W91JqqlkdsVhh5sgR3z6hKjgVFeYyL89/u32Gk2H4t9GSn28ua2vNZagKjiUnR3VY57HOQReVpBgFnL179+q5556LxaEBAPAXGGLsA4hD3d3XqnpY4SWw6yg729d9ZHVBhargWEIFHLtQAccSqoJj6dix7jHtY4eCnSNB8TRxAEDzFqqLyl7BCfyjH1jBCQwjHo/UqZP/Z6EqOJbAgJOW5ruRn2RWiQJnIJ9wgv/7UDf6s9RXwbEUFtbdJwERcAAAzVdNje9hmIFdVPYxOOEqOIFVEKludeV4Kzj2mw5KdcNMQ86RkeE/+ytcBScpSerate4+CYiAAwBovqyb30l1H6Z5PGNwgnUn2Ss4ycl19wlXwZGknj1966ecUnd7uAqO5N9NFayCYw9JJ57oP5YogUU0i+qKK66od3uZNaUOAIBYsrqnpLpjcOzTxENVcIJNEbfYg0NWVt3ZS4EVnGB3+D/jDGnFCnO9T5/6zyEFDzjZ2dJ335nrwSo4Q4ZIjz9urvfoUXd7gooo4GQFptYg27kPDgAg5uz3uUn6b6dEsC6qUGNwLMG6qOwVnGB/9wIHAAer4PTr51sPVsGxB5z27euO+7HasXGjuR6sgjNkiG892PYExX1wAADNV7AA05AuqsBAE66CExhmJDOQnHSS7x427drV3eeMM3zrvXvX3W5v9803+0Ka3UMPSe+9Z6537153uz0UhXjCQCKKaAzO0qVLNWfOHL/PXnzxRXXp0kU5OTm68cYbeVwDACD2gnVBWY8y2LMndMAJHIgbrGvHHhyuvLLudo9HmjvXDDbnnhs8nPTpY3YrdegQPOBI0l//Ko0ZI913X/DtQ4dKX30l/ec/Uq9ewfeZOlU6+WTp178Ovj0BRVTBeeihh/SDH/xAF198sSTps88+0/XXX6+xY8eqV69eeuyxx5Sfn68HH3wwmm0FAMBf4BRxyddVVFoaegxO585m8LDu/tu/f91jn3aaedyePaU77wx+/p49zQpOqHvPpKVJ69cHb4OluDj453ZdupivUH75S/MFr4gqOGvWrNHw4cO972fOnKlBgwbpb3/7m8aPH68pU6botddei1ojAQAIKljAsQb7Hjniu0lfYADxePwH/drHylhycqQdO6SPPqp/ZlKbNv73uwnUsWPwwcGIqYgCzv79+5VrGy2+aNEiFRUVed8PGDBA27dvb3zrAACoT7AuqBYtfLORVq40l4GzlST/+8t07hz8+PY7GqNZiSjg5ObmauvWrZKko0eP6tNPP9WZZ57p3V5ZWanU1NTotBAAgFCCVXAkXzfVunXmMtjdfR980Bw3M3Zs3SngaPYiGoNz4YUX6t5779Xvf/97zZo1Sy1bttTZZ5/t3b527Vp169Ytao0EACCo+gLOF1/43gcLOAMHmoN3mVrtShEFnIcfflhXXHGFzjnnHLVu3VovvPCC0mz9k88//7zOP//8qDUSAICgQt3nJvCeNKGez3TiidFvE+JCRAGnQ4cOWrx4scrLy9W6dWslBwyuev3119U62E2TAACI1NGjZpeS/YGVoWZJ2e8qnJ7OIN8E1KhnUWVlZdUJN5LUrl07v4oOAACNsnu31K2bdOaZ0rFjvs/DjcGRpIICxtgkIB62CQCIf488In37rTkr6tVXfZ+H6qKyP8Qy8LEMSAgEHABAfKuulp55xvfeerCkFLqLatQo32fBbuIH14toDA4AAE1m1y7zpn2Wdeukmhrz5nqhuqgyM6Vt26QXXpD+53+arq2IG1RwAADxrbTUXObmmgOGq6sl62ayobqoJPP5T7/6VegZVHA1Ag4AIL6VlJjL/HzfQzI3bzaXBw+ay1DPgkLCIuAAAOKbvYLTvbu5vmWLudyzx1y2a9f07UJcYwwOACC+2QOOFWSsgGM9DZz73CAAAQcAEN+sLqq8PN9DMQk4CIMuKgBAfLNXcHr0MNe3bDEHG5eVme8JOAhAwAEAxDd7Bccag/Pll+b0ccl8fANjcBCAgAMAiG/2Ck5hoZSaKlVVSatXm5936GCGHMCGfxEAgPhmVWpycswHbXbpYr7/6CNzSfcUgiDgAADiW0WFuczKMpdWNxUBB/Ug4AAA4tfRo+ZgYsn30ExroPHSpeaSgIMgCDgAgPhVWelbb93aXFoBp6rKXBJwEAQBBwAQv6yAk5Fhjr+RpKIi/33y8pq2TWgWCDgAgPhlBRyre0oyn0dljcORpKuuato2oVkg4AAA4lewgCNJf/qTOV38L3/xdVkBNjyqAQAQvw4cMJeBAeeSS6QjR7j/DULiXwYAIH6FquBIhBvUi38dAID4ZQUcawYV0EAEHABA/KqvggPUg4ADAIhfBBxEiIADAIhfoQYZA2EQcAAA8YsKDiJEwAEAxC8GGSNCBBwAQPyigoMIEXAAAPGLgIMIEXAAAPGLQcaIEAEHABC/KirMJQEHx4mAAwCIX3v3msv27Z1tB5odAg4AID4ZhrRnj7neoYOzbUGzQ8ABAMSnigrp2DFznYCD40TAAQDEJ6t607q1lJHhbFvQ7DTLgPPggw/K4/H4vU4++WSnmwUAiKbdu80l1RtEIMXpBkTqlFNO0X/+8x/v+5SUZvtTAADBMP4GjdBsU0FKSory8vKcbgYAIFYIOGiEZtlFJUmbN29Wfn6+unbtqtGjR2vbtm0h962qqlJFRYXfCwAQ5wg4aIRmGXAGDRqk6dOna+7cuXrqqae0detWnX322aq0bukdYNKkScrKyvK+CgsLm7jFAIDjRsBBIzTLgFNUVKQf//jHOvXUUzVy5Ei98847Kisr02uvvRZ0/wkTJqi8vNz72r59exO3GABw3Ag4aIRmOwbHLjs7W9/73ve0ZcuWoNvT09OVnp7exK0CADQKs6jQCM2yghPowIED+vLLL9WpUyenmwIAiIbqaunDD831Hj2cbQuapWYZcO68804tWrRIX3/9tT766CONGjVKycnJuvrqq51uGgAgGt5913wOVU6ONGyY061BM9Qsu6i+/fZbXX311dq7d686duyooUOHatmyZerYsaPTTQMANNbHH0tjx5rr//M/Evc5QwQ8hmEYTjeiqVVUVCgrK0vl5eXKzMx0ujkAAEtVlXTaadKmTVKvXtK//y0VFDjdKsSJ4/n7TSwGAMSPqVPNcJOba47BadvW6RahmWqWY3AAAC506JD06KPm+iOPEG7QKAQcAIDz9u+XRo2SSkulE0+UfvYzp1uEZo4uKgCAsw4dks48U/riC6lVK2naNCk11elWoZkj4AAAnLV4sRlu2reX5s2T+vVzukVwAbqoAADOsm7od/HFhBtEDQEHAOCsJUvM5dChzrYDrkLAAQA459gxaflyc33IEGfbAlch4AAAnFNSIh0+bN6tuGdPp1sDFyHgAACcU1JiLvPypCT+JCF6+NcEAHCOFXByc51tB1yHgAMAcE5pqbnMy3O2HXAdAg4AwDn2Liogigg4AADn0EWFGCHgAACcQwUHMULAAQA4hzE4iBECDgDAOVRwECMEHACAcxiDgxgh4AAAnFFZab4kKT/f2bbAdQg4AABn7NxpLjMzpdatnW0LXIeAAwBwxo4d5vKEE5xtB1yJgAMAcIYVcOieQgwQcAAAzrC6qKjgIAYIOAAAZ9BFhRgi4AAAnEEXFWKIgAMAcAYVHMQQAQcA0PSOHJE2bDDXu3Rxti1wJQIOAKDpzZtn3uSvsFA69VSnWwMXIuAAAJrWwYPSn/9srv/oR1ISf4oQffyrAgA0rf/9X2nRIiktTRo3zunWwKUIOACAprN/v/Tqq+b67NlSnz7OtgeuRcABADSdV181Bxifeqp0/vlOtwYuRsABADSdefPM5TXXSB6Ps22BqxFwAABNwzCkpUvN9aFDnW0LXI+AAwBoGl9+Ke3ZYw4uPuMMp1sDlyPgAACahlW9OeMMKT3d2bbA9Qg4AICmMWuWuTznHEebgcRAwAEAxN7+/dKcOeb6T3/qbFuQEAg4AIDY2r1b+uEPpaNHpb59eTQDmgQBBwAQO7W10k9+Iq1cKbVvL/31r063CAkixekGAABc7KOPpPffl1q1khYvlnr3drpFSBBUcAAAsbN4sbm88ELCDZoUAQcAEDsffGAuubEfmhgBBwAQGzU1ZheVJJ19trNtQcIh4AAAYmPTJqmiwhx/07ev061BgiHgAABi49NPzeX3vy+lMKcFTYuAAwCIjVWrzOXppzvbDiQkAg4AIDYIOHAQAQcAEH1Hj/q6qHhyOBxAwAEARN/f/y6Vl0udOkmnnOJ0a5CACDgAgOh6/XXpF78w12+9VUpNdbY9SEgMawcARM+//iVddZX5DKqePaWbbnK6RUhQVHAAANFhGNJdd5nhZswYae1aqW1bp1uFBEXAAQBEx8cfSxs3Si1aSE88IaWlOd0iJDACDgAgOp591lxecYWUleVsW5DwCDgAgMbbvVt66SVz3RpgDDiIgAMAaJx335Uuu0w6ckTq148nhyMuMIsKABC5v/7VnAouSW3aSH/8o+TxONsmQFRwAACR+uILafx4c/2nPzVnTZ1zjrNtAv6LCg4AIDJ//KNUXS2NHCm9/DKVG8QVKjgAgOP3xRfSCy+Y6/ffT7hB3CHgAAAa7quvpHvukc47T6qqkoYPZ1Ax4hJdVACAhtm0yQwze/aY7wsKzKnhVG8Qh6jgAADC++orc6zNnj3SaadJM2dKGzaYTwsH4hAVHABAcBUV0qOPSm+9ZT6CoaZG6tFD+ve/pZwcp1sH1IuAAwDwd+SItGKFNG6c9OWXvs+HDTMHFhNu0Aw06y6qqVOn6qSTTlJGRoYGDRqkFStWON0kAGieysulP/xBOvVUqWVL8342X34pde4svfKK2UW1cKF00klOtxRokGZbwfn73/+u8ePH6+mnn9agQYP0+OOPa+TIkdq0aZNy+H8XAFBXdbX0zTdSSYlUWiotWiQtWWKul5aaXVCWNm3Mxy9MmSK1betcm4EIeQzDMJxuRCQGDRqkAQMG6K9//askqba2VoWFhbr11lt177331vvdiooKZWVlqby8XJmZmdFrVGWltG9f+P2O55I3dN9o79dcjsm5OXeinPvYMXNa9pEj5tK+Huyzw4fN0LJ7t7m+f7+0bZt/iAnUu7d0xx3SxRdLubnMjkLcOZ6/382ygnP06FGtXLlSEyZM8H6WlJSkESNGaOnSpXX2r6qqUlVVlfd9RUVFbBo2c6Z0442xOTYAREOLFlJ+vtS+vXT66dKIEVKXLuZsqPx8p1sHRE2zDDh79uxRTU2NcnNz/T7Pzc3V559/Xmf/SZMm6aGHHop9w5KTzf/xaIjj+X9GDd032vs1l2Nybs6dCOdOSZHS06WMDP9lfZ917GgOCG7ZUsrMlLp2NYMMlRkkgGYZcI7XhAkTNN56IJzMCk5hYWH0TzRunPkCAACOapYBp0OHDkpOTlZpaanf56WlpcrLy6uzf3p6utLT05uqeQAAwGHNcpp4Wlqa+vXrpwULFng/q62t1YIFCzR48GAHWwYAAOJBs6zgSNL48eM1ZswY9e/fXwMHDtTjjz+ugwcP6rrrrnO6aQAAwGHNNuD85Cc/0e7du/XAAw+opKRE3//+9zV37tw6A48BAEDiabb3wWmMmN0HBwAAxMzx/P1ulmNwAAAA6kPAAQAArkPAAQAArkPAAQAArkPAAQAArkPAAQAArkPAAQAArkPAAQAArkPAAQAArtNsH9XQGNbNmysqKhxuCQAAaCjr73ZDHsKQkAGnsrJSklRYWOhwSwAAwPGqrKxUVlZWvfsk5LOoamtrtXPnTrVp00Yejyfm56uoqFBhYaG2b9/Os68ixDVsPK5h43ENG49r2HiJfA0Nw1BlZaXy8/OVlFT/KJuErOAkJSWpoKCgyc+bmZmZcP8Yo41r2Hhcw8bjGjYe17DxEvUahqvcWBhkDAAAXIeAAwAAXIeA0wTS09P1m9/8Runp6U43pdniGjYe17DxuIaNxzVsPK5hwyTkIGMAAOBuVHAAAIDrEHAAAIDrEHAAAIDrEHAAAIDrEHCawNSpU3XSSScpIyNDgwYN0ooVK5xuUlyYNGmSBgwYoDZt2ignJ0eXX365Nm3a5LfPkSNHVFxcrPbt26t169a68sorVVpa6rfPtm3bdNFFF6lly5bKycnRXXfdpWPHjjXlT4kLkydPlsfj0e233+79jOvXMDt27NA111yj9u3bq0WLFurbt68++eQT73bDMPTAAw+oU6dOatGihUaMGKHNmzf7HWPfvn0aPXq0MjMzlZ2dreuvv14HDhxo6p/S5GpqavTrX/9aXbp0UYsWLdStWzc9/PDDfs8K4vrVtXjxYl1yySXKz8+Xx+PRrFmz/LZH65qtXbtWZ599tjIyMlRYWKhHH3001j8tfhiIqZkzZxppaWnG888/b6xfv9644YYbjOzsbKO0tNTppjlu5MiRxrRp04x169YZq1evNi688EKjc+fOxoEDB7z73HTTTUZhYaGxYMEC45NPPjHOPPNM46yzzvJuP3bsmNGnTx9jxIgRxqpVq4x33nnH6NChgzFhwgQnfpJjVqxYYZx00knGqaeeatx2223ez7l+4e3bt8848cQTjbFjxxrLly83vvrqK2PevHnGli1bvPtMnjzZyMrKMmbNmmWsWbPGuPTSS40uXboYhw8f9u5zwQUXGKeddpqxbNky44MPPjC6d+9uXH311U78pCb1yCOPGO3btzfmzJljbN261Xj99deN1q1bG0888YR3H65fXe+8845x//33G2+88YYhyXjzzTf9tkfjmpWXlxu5ubnG6NGjjXXr1hkzZswwWrRoYTzzzDNN9TMdRcCJsYEDBxrFxcXe9zU1NUZ+fr4xadIkB1sVn3bt2mVIMhYtWmQYhmGUlZUZqampxuuvv+7dZ+PGjYYkY+nSpYZhmP8jkZSUZJSUlHj3eeqpp4zMzEyjqqqqaX+AQyorK40ePXoY8+fPN8455xxvwOH6Ncw999xjDB06NOT22tpaIy8vz3jssce8n5WVlRnp6enGjBkzDMMwjA0bNhiSjI8//ti7z7vvvmt4PB5jx44dsWt8HLjooouMcePG+X12xRVXGKNHjzYMg+vXEIEBJ1rX7MknnzTatm3r99/le+65x+jZs2eMf1F8oIsqho4ePaqVK1dqxIgR3s+SkpI0YsQILV261MGWxafy8nJJUrt27SRJK1euVHV1td/1O/nkk9W5c2fv9Vu6dKn69u2r3Nxc7z4jR45URUWF1q9f34Std05xcbEuuugiv+skcf0aavbs2erfv79+/OMfKycnR6effrr+9re/ebdv3bpVJSUlftcxKytLgwYN8ruO2dnZ6t+/v3efESNGKCkpScuXL2+6H+OAs846SwsWLNAXX3whSVqzZo2WLFmioqIiSVy/SETrmi1dulTDhg1TWlqad5+RI0dq06ZN2r9/fxP9Guck5MM2m8qePXtUU1Pj98dDknJzc/X555871Kr4VFtbq9tvv11DhgxRnz59JEklJSVKS0tTdna23765ubkqKSnx7hPs+lrb3G7mzJn69NNP9fHHH9fZxvVrmK+++kpPPfWUxo8fr/vuu08ff/yx/vd//1dpaWkaM2aM9zoEu07265iTk+O3PSUlRe3atXP9dbz33ntVUVGhk08+WcnJyaqpqdEjjzyi0aNHSxLXLwLRumYlJSXq0qVLnWNY29q2bRuT9scLAg7iQnFxsdatW6clS5Y43ZRmY/v27brttts0f/58ZWRkON2cZqu2tlb9+/fX7373O0nS6aefrnXr1unpp5/WmDFjHG5d/Hvttdf0yiuv6NVXX9Upp5yi1atX6/bbb1d+fj7XD46iiyqGOnTooOTk5DqzVkpLS5WXl+dQq+LPLbfcojlz5uj9999XQUGB9/O8vDwdPXpUZWVlfvvbr19eXl7Q62ttc7OVK1dq165dOuOMM5SSkqKUlBQtWrRIU6ZMUUpKinJzc7l+DdCpUyf17t3b77NevXpp27ZtknzXob7/Hufl5WnXrl1+248dO6Z9+/a5/jredddduvfee3XVVVepb9++uvbaa3XHHXdo0qRJkrh+kYjWNUv0/34TcGIoLS1N/fr104IFC7yf1dbWasGCBRo8eLCDLYsPhmHolltu0Ztvvqn33nuvTim1X79+Sk1N9bt+mzZt0rZt27zXb/Dgwfrss8/8/os+f/58ZWZm1vmj5TbDhw/XZ599ptWrV3tf/fv31+jRo73rXL/whgwZUuf2BF988YVOPPFESVKXLl2Ul5fndx0rKiq0fPlyv+tYVlamlStXevd57733VFtbq0GDBjXBr3DOoUOHlJTk/6ckOTlZtbW1krh+kYjWNRs8eLAWL16s6upq7z7z589Xz549Xd89JYlp4rE2c+ZMIz093Zg+fbqxYcMG48YbbzSys7P9Zq0kqptvvtnIysoyFi5caHz33Xfe16FDh7z73HTTTUbnzp2N9957z/jkk0+MwYMHG4MHD/Zut6Y5n3/++cbq1auNuXPnGh07dkyoac529llUhsH1a4gVK1YYKSkpxiOPPGJs3rzZeOWVV4yWLVsaL7/8snefyZMnG9nZ2cZbb71lrF271rjsssuCTtk9/fTTjeXLlxtLliwxevTo4eppzpYxY8YYJ5xwgnea+BtvvGF06NDBuPvuu737cP3qqqysNFatWmWsWrXKkGT86U9/MlatWmV88803hmFE55qVlZUZubm5xrXXXmusW7fOmDlzptGyZUumiSN6/vKXvxidO3c20tLSjIEDBxrLli1zuklxQVLQ17Rp07z7HD582PjlL39ptG3b1mjZsqUxatQo47vvvvM7ztdff20UFRUZLVq0MDp06GD86le/Mqqrq5v418SHwIDD9WuYt99+2+jTp4+Rnp5unHzyycb//d//+W2vra01fv3rXxu5ublGenq6MXz4cGPTpk1+++zdu9e4+uqrjdatWxuZmZnGddddZ1RWVjblz3BERUWFcdtttxmdO3c2MjIyjK5duxr333+/39Rkrl9d77//ftD//RszZoxhGNG7ZmvWrDGGDh1qpKenGyeccIIxefLkpvqJjvMYhu12kwAAAC7AGBwAAOA6BBwAAOA6BBwAAOA6BBwAAOA6BBwAAOA6BBwAAOA6BBwAAOA6BBwAAOA6BBwAAOA6BBwAAOA6BBwACWnOnDnq0qWLBg4cqM2bNzvdHABRxrOoACSknj17aurUqVq/fr2WLl2qmTNnOt0kAFFEBQdAQmrfvr26d++uk046SWlpaU43B0CUpTjdAACIpuuuu04nnHCCJk6cGHa/bt26KTc3V+vWrWui1gFoKnRRAXCNmpoa5eXl6V//+pcGDhwYcr9jx47p+9//vi655BJNnTpV5eXl8ng8TdhSALFGFxWAuPT111/L4/HUeZ177rkhv/PRRx8pNTVVAwYMqPfYTz/9tLp27ari4mJVVlbqq6++inLrATiNLioAcamwsFDfffed931JSYlGjBihYcOGhfzO7Nmzdckll9Rbjdm3b58efvhhLVy4UAUFBcrKytLq1avVrVu3qLYfgLOo4ACIS8nJycrLy1NeXp6ys7N10003afDgwXrwwQdDfuett97SpZdeWu9xf/Ob32jUqFHq1auXJKl3795as2ZNNJsOIA5QwQEQ98aNG6fKykrNnz9fSUnB/3/Zxo0btXPnTg0fPjzkcTZs2KCXX35ZGzdu9H7Wp08frV69OtpNBuAwAg6AuDZx4kTNmzdPK1asUJs2bULuN3v2bP3whz9URkZGyH3uuOMOlZWVqaCgwPtZbW2tCgsLo9pmAM4j4ACIW//85z/129/+Vu+++27YMTJvvfWWbrzxxpDb58yZo5UrV2rVqlVKSfH9T9/HH3+scePGaf/+/Wrbtm3U2g7AWUwTBxCX1q1bp0GDBmn8+PEqLi72fp6WlqZ27dr57btr1y4VFBRo586d6tChQ51jVVdXq0+fPho3bpzuuecev23btm3TiSeeqPfff7/eGVoAmhcGGQOIS5988okOHTqkiRMnqlOnTt7XFVdcUWfft99+WwMHDgwabiTpL3/5i8rKynTLLbfU2VZYWKiWLVsyDgdwGSo4AJq9Sy+9VEOHDtXdd9/tdFMAxAkqOACavaFDh+rqq692uhkA4ggVHAAA4DpUcAAAgOsQcAAAgOsQcAAAgOsQcAAAgOsQcAAAgOsQcAAAgOsQcAAAgOsQcAAAgOsQcAAAgOv8fxV5HuKzD/BEAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "air = SLD(0)\n",
    "\n",
    "si = SLD(21.861 + 0.103j, \"Si\")  # 19.81 + 0.093j\n",
    "sio2 = SLD(21.2632 + 0.058j, \"SiO2\")  # 18.709\n",
    "\n",
    "sio2_l = sio2(47, 1.2) #992 47 - pilatus fit\n",
    "inter_sld = SLD(16.9,\"iner_SLD\")\n",
    "inter_l = inter_sld(5.33, 7.6)\n",
    "\n",
    "#         nlayers,\n",
    "#         monolayer_thickness,\n",
    "#         rel_roughness,\n",
    "#         sld_box1,   # <----- closest to phase layer\n",
    "#         sld_box2,\n",
    "#         thick_frac,\n",
    "#         sig1_rel_pos,\n",
    "#         sig1_width,\n",
    "#         sig2_rel_pos,\n",
    "#         sig2_width,\n",
    "\n",
    "# this will have p1 closest to the interface layer, then p2, etc.\n",
    "# if you want it to be the other way around then you should swap p1 and p2 in the line below.\n",
    "# note that funny things will happen if sig1_rel_pos and sig2_rel_pos are allowed to be greater\n",
    "# than the number of monolayers\n",
    "pol_slab = PTCDI(50, 20.8, 0.1, 8, 13, 0.5, 10, 1, 10, 1)\n",
    "\n",
    "s = air | pol_slab | inter_l | sio2_l | si(0, 1)\n",
    "\n",
    "# XRR has very good q resolution\n",
    "model = ReflectModel(\n",
    "    s, bkg=1e-7, dq = 1, dq_type='pointwise') \n",
    "# model.scale.setp(bounds=(0.8, 1.2), vary=True)\n",
    "model.bkg.setp(bounds=(1e-10, 1e-5), vary=True)\n",
    "model.dq.setp(bounds=(0, 2), vary=True)\n",
    "# model.q_offset.setp(bounds=(0, 0.02), vary=True)\n",
    "\n",
    "q2 = np.linspace(0.01, 0.4, 256)\n",
    "plt.plot(q2, model(q2))\n",
    "plt.xlabel(\"Q\")\n",
    "plt.ylabel(\"Reflectivity\")\n",
    "plt.yscale(\"log\")\n",
    "s.plot();\n",
    "\n",
    "###########################\n",
    "# set up varying parameters\n",
    "###########################\n",
    "\n",
    "# vary the monolayer sld's\n",
    "# by setting how these Parameters change you'll be affecting\n",
    "# polymer1, pol_slab.sld_box1, pol_slab.sld_box2, inter_l.sld.real, etc.\n",
    "#sio2_l.thick.setp(vary=True, bounds=(1, 1000))\n",
    "#sio2_l.rough.setp(vary=True, bounds=(1, 3))\n",
    "\n",
    "inter_l.thick.setp(vary=True, bounds=(3, 20))\n",
    "inter_l.rough.setp(vary=True, bounds=(1, 8))\n",
    "inter_l.sld.real.setp(vary=True, bounds=(5, 21))\n",
    "\n",
    "# vary stuff to do with multilayer\n",
    "#pol_slab.nlayers.setp(vary=True, bounds=(1, 30))\n",
    "pol_slab.monolayer_thickness.setp(vary=True, bounds=(19, 24))\n",
    "pol_slab.sld_box1.setp(vary=True, bounds=(3, 8))\n",
    "pol_slab.sld_box2.setp(vary=True, bounds=(14, 18))\n",
    "\n",
    "pol_slab.sig1_rel_pos.setp(vary=True, bounds=(0, 30))\n",
    "pol_slab.sig1_width.setp(vary=True, bounds=(0.2, 1))\n",
    "\n",
    "pol_slab.sig2_rel_pos.setp(vary=True, bounds=(-2, 30))\n",
    "pol_slab.sig2_width.setp(vary=True, bounds=(0.1, 3))\n",
    "#pol_slab.rel_roughness.setp(vary=True, bounds=(0.1, 0.35))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "dcd8265b-4dd4-405d-b3d5-dfef94c0e3df",
   "metadata": {},
   "outputs": [],
   "source": [
    "objective = Objective(model, data, transform=Transform(\"logY\"))\n",
    "#objective = Objective(model, data, transform=Transform(\"YX4\"))\n",
    "#objective = Objective(model, data, transform=Transform(\"YX2\"))\n",
    "fitter = CurveFitter(objective)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "0b16d2b5-b0c6-4e48-82b9-8f9592adfe61",
   "metadata": {},
   "outputs": [],
   "source": [
    "# setup a constraint that:\n",
    "# nlayers - 1 - sig1_rel_pos has to be greater than 0\n",
    "# and\n",
    "# nlayers - 1 - sig2_rel_pos has to be greater than 0\n",
    "class DEC(object):\n",
    "    def __init__(self, pars, objective):\n",
    "        self.pars = pars\n",
    "        self.objective = objective\n",
    "\n",
    "    def __call__(self, x):\n",
    "        self.objective.setp(x)\n",
    "        nlayers = np.round(self.pars[0].value)\n",
    "        sig1_rel_pos = self.pars[1].value\n",
    "        sig2_rel_pos = self.pars[2].value\n",
    "        \n",
    "        return np.array(nlayers - 1 - sig1_rel_pos, nlayers - 1 - sig1_rel_pos)\n",
    "\n",
    "pars = (pol_slab.nlayers, pol_slab.sig1_rel_pos, pol_slab.sig2_rel_pos)\n",
    "dec = DEC(pars, objective)\n",
    "\n",
    "from scipy.optimize import NonlinearConstraint\n",
    "constraint = NonlinearConstraint(dec, 0, np.inf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "5ed8877c-a202-4aba-bb93-cb6888e1e969",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2.212351253966237: : 147it [07:02,  2.88s/it] \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "________________________________________________________________________________\n",
      "Structure:                \n",
      "solvent: None\n",
      "reverse structure: False\n",
      "contract: 0\n",
      "\n",
      "________________________________________________________________________________\n",
      "Parameters:       ''       \n",
      "<Parameter:  ' - thick'   , value=0 (fixed)  , bounds=[-inf, inf]>\n",
      "________________________________________________________________________________\n",
      "Parameters:       ''       \n",
      "<Parameter:   ' - sld'    , value=0 (fixed)  , bounds=[-inf, inf]>\n",
      "<Parameter:   ' - isld'   , value=0 (fixed)  , bounds=[-inf, inf]>\n",
      "<Parameter:  ' - rough'   , value=0 (fixed)  , bounds=[-inf, inf]>\n",
      "<Parameter:' - volfrac solvent', value=0 (fixed)  , bounds=[0.0, 1.0]>\n",
      "________________________________________________________________________________\n",
      "Parameters:      None      \n",
      "<Parameter:      ''       , value=50 (fixed)  , bounds=[-inf, inf]>\n",
      "<Parameter:      ''       , value=20.8205 +/- 0.0106, bounds=[19.0, 24.0]>\n",
      "<Parameter:      ''       , value=0.1 (fixed)  , bounds=[-inf, inf]>\n",
      "<Parameter:      ''       , value=7.99515 +/- 0.487, bounds=[3.0, 8.0]>\n",
      "<Parameter:      ''       , value=14.0096 +/- 0.405, bounds=[14.0, 18.0]>\n",
      "<Parameter:      ''       , value=0.5 (fixed)  , bounds=[-inf, inf]>\n",
      "<Parameter:      ''       , value=23.6232 +/- 0.19 , bounds=[0.0, 30.0]>\n",
      "<Parameter:      ''       , value=0.99962 +/- 0.0685, bounds=[0.2, 1.0]>\n",
      "<Parameter:      ''       , value=20.4276 +/- 2.25 , bounds=[-2.0, 30.0]>\n",
      "<Parameter:      ''       , value=0.100035 +/- 0.0258, bounds=[0.1, 3.0]>\n",
      "________________________________________________________________________________\n",
      "Parameters:   'iner_SLD'   \n",
      "<Parameter:'iner_SLD - thick', value=3.88628 +/- 0.481, bounds=[3.0, 20.0]>\n",
      "________________________________________________________________________________\n",
      "Parameters:   'iner_SLD'   \n",
      "<Parameter:'iner_SLD - sld', value=14.2407 +/- 0.575, bounds=[5.0, 21.0]>\n",
      "<Parameter:'iner_SLD - isld', value=0 (fixed)  , bounds=[-inf, inf]>\n",
      "<Parameter:'iner_SLD - rough', value=7.9716 +/- 1.26 , bounds=[1.0, 8.0]>\n",
      "<Parameter:'iner_SLD - volfrac solvent', value=0 (fixed)  , bounds=[0.0, 1.0]>\n",
      "________________________________________________________________________________\n",
      "Parameters:     'SiO2'     \n",
      "<Parameter:'SiO2 - thick' , value=47 (fixed)  , bounds=[-inf, inf]>\n",
      "________________________________________________________________________________\n",
      "Parameters:     'SiO2'     \n",
      "<Parameter: 'SiO2 - sld'  , value=21.2632 (fixed)  , bounds=[-inf, inf]>\n",
      "<Parameter: 'SiO2 - isld' , value=0.058 (fixed)  , bounds=[-inf, inf]>\n",
      "<Parameter:'SiO2 - rough' , value=1.2 (fixed)  , bounds=[-inf, inf]>\n",
      "<Parameter:'SiO2 - volfrac solvent', value=0 (fixed)  , bounds=[0.0, 1.0]>\n",
      "________________________________________________________________________________\n",
      "Parameters:      'Si'      \n",
      "<Parameter: 'Si - thick'  , value=0 (fixed)  , bounds=[-inf, inf]>\n",
      "________________________________________________________________________________\n",
      "Parameters:      'Si'      \n",
      "<Parameter:  'Si - sld'   , value=21.861 (fixed)  , bounds=[-inf, inf]>\n",
      "<Parameter:  'Si - isld'  , value=0.103 (fixed)  , bounds=[-inf, inf]>\n",
      "<Parameter: 'Si - rough'  , value=1 (fixed)  , bounds=[-inf, inf]>\n",
      "<Parameter:'Si - volfrac solvent', value=0 (fixed)  , bounds=[0.0, 1.0]>\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# only use differential_evolution, this is because nlayers has to be an integer variable\n",
    "# and diffev is able to cope with that, other minimisers can't.\n",
    "fitter.fit(\"differential_evolution\")\n",
    "\n",
    "# if you want to use the constraint\n",
    "#fitter.fit(\"differential_evolution\", constraints=(constraint,))\n",
    "\n",
    "print(s)\n",
    "\n",
    "objective.plot()\n",
    "# plt.yscale('log')\n",
    "plt.xlabel(\"Q / $\\\\AA^{-1}$\")\n",
    "plt.ylabel(\"Reflectivity\")\n",
    "#plt.yscale(\"log\")\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "459acc11-00ca-44bf-a692-68a65db9d79c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0\n",
      "0.036154804529326734\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "s.plot()\n",
    "print(objective.model.q_offset.value)\n",
    "print(objective.model.dq.value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "280db19f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "s1 = s\n",
    "\n",
    "z, sld1 = s1.sld_profile()\n",
    "\n",
    "labelsx = ['700', '600', '400', '200', '0']\n",
    "\n",
    "fig = plt.figure(figsize=(4, 3), dpi=100) \n",
    "ax = fig.add_subplot(111)\n",
    "# Plot the SLD profile\n",
    "#ax.plot(z, sld1, 'r')\n",
    "ax.plot(z, sld1, 'r')\n",
    "ax.set_xlabel('Distance (Å)')\n",
    "ax.set_ylabel('SLD ($10^{-6}$ Å⁻²)')\n",
    "ax.set_xlim([500,1100])\n",
    "ax.set_xticks([350, 450, 650, 850, 1050], labelsx)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "71320fb4-fded-4bf5-a240-7f538cefa769",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "56544125",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bcac831e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "TensorFlow2",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.18"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
